Shortened telomeres in individuals with abuse in alcoholconsumption

Sofia Pavanello1,*, Mirjam Hoxha2, Laura Dioni2, Pier Alberto Bertazzi2, Rossella Snenghi3,Alessandro Nalesso3, Santo Davide Ferrara3, Massimo Montisci3, and Andrea Baccarelli41 Occupational Health Section, Department of Environmental Medicine and Public Health,Universita di Padova, Via Giustiniani 2, 35128 Padova, Italy2 Center of Molecular and Genetic Epidemiology, Universita di Milano & IRCCS Ca’ GrandaMaggiore Policlinico Hospital Foundation, Via San Barnaba 8, 20122 Milan, Italy3 Forensic Toxicology and Antidoping Unit, Department of Environmental Medicine and PublicHealth, Universita di Padova, Via Falloppio, 50, 35121 Padova, Italy4 Exposure Epidemiology and Risk Program, Department of Environemnatl Health, HarvardSchool of Public Health, P.O. Box 15698, 401 Park Dr, Landmark Center-415-West Boston, MA02215

AbstractAlcohol abuse leads to earlier onset of aging-related diseases, including cancer at multiple sites.Shorter telomere length (TL) in peripheral blood leucocytes (PBLs), a marker of biological aging,has been associated with alcohol-related cancer risks. Whether alcohol abusers exhibit acceleratedbiological aging, as reflected in PBL-TL, has never been examined.

To investigated the effect of alcohol abuse on PBL-TL and its interaction with alcohol metabolicgenotypes, we examined 200 drunk-driving traffic offenders diagnosed as alcohol abusers as perthe Diagnostic and Statistical Manual of Mental Disorders [DSM-IV-TR] and enrolled in aprobation program, and 257 social drinkers (controls). We assessed alcohol intake using self-reported drink-units/day and conventional alcohol abuse biomarkers (serum γ-glutamyltrasferase[GGT] and mean corpuscular volume of erythrocytes [MCV]). We used multivariable models tocompute TL geometric means (GM) adjusted for age, smoking, BMI, diet, job at elevated risk ofaccident, genotoxic exposures.

TL was nearly halved in alcohol abusers compared to controls (GMs 0.42 vs. 0.87 relative T/Sratio; P<0.0001) and decreased in relation with increasing drink-units/day (P-trend=0.003).Individuals drinking >4 drink-units/day had substantially shorter TL than those drinking 4 drink-units/day (GMs 0.48 vs. 0.61 T/S, P=0.002). Carriers of the common ADH1B*1/*1 (rs1229984)genotype were more likely to be abusers (P=0.008), reported higher drink-units/day (P=0.0003),and exhibited shorter TL (P<0.0001). The rs698 ADH1C and rs671 ALDH2 polymorphisms werenot associated with TL.

The decrease in PBL-TL modulated by the alcohol metabolic genotype ADH1B*1/*1 mayrepresent a novel mechanism potentially related to alcohol carcinogenesis in alcohol abusers.

*To whom correspondence and reprint requests should be sent, phone: +390498216600; fax: +390498212542;,sofia.pavanello@unipd.it.Conflicts of interest: No conflicts of interest for any authorsFinancial Disclosures: None reported.

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Published in final edited form as:Int J Cancer. 2011 August 15; 129(4): 983–992. doi:10.1002/ijc.25999.

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INTRODUCTIONThe rates of almost any type of cancer increase with age. Alcohol abusers tend to lookhaggard, and it is commonly thought that heavy drinking leads to premature aging andearlier onset of age-related diseases[1]. Abuse in alcohol drinking is a global healthpriority[2] that has been associated with cancer at multiple sites [3]. Taken together thesedata suggest that alcohol abuse may accelerate biological processes related to aging.Nevertheless, the exact mechanisms of how alcohol drinking exerts these effects are not wellknown, including whether alcohol accelerates biological aging at a cellular level.

All the cells in our body have a biological clock in telomeres, DNA sequences located at theends of chromosomes that are involved in maintaining genomic stability and regulatingcellular proliferation [4]. Telomere length (TL) in proliferating tissues, which can beconveniently measured in peripheral blood leucocytes (PBLs), is longest at birth andshortens progressively as individuals age [5]. Shorter TL has been associated with risk ofseveral age-related diseases and is widely accepted as a marker of biological cellular aging[6]. Epidemiology retrospective [7–12] and prospective [13,14] studies have shown thatamong subjects of the same age, those with shorter telomeres in PBLs have higher risk ofcancer at multiple sites [6–14]. Oxidative stress [15,16] and inflammation [16,17], twomechanisms that accelerate telomere shortening [18,19], have been linked with heavyalcohol consumption (for a review see [20]) as well as with the risk of cancer at multiplesites [21,22]. However, whether alcohol drinking is associated with telomere shortening hasnever been evaluated.

Mechanisms of alcohol-induced cancer are closely related to the metabolism of ethanol [23].Alcohol is principally metabolized by the alcohol dehydrogenase (ADH) to acetaldehydethat is further oxidized to acetate by aldehyde dehydrogenase (ALDH2) enzymes, whichexist in several polymorphic variants [24]. Among Caucasians, variants in ADH genes arecommon, but very rare in ALDH2 [24]. Individuals who carry highly active alleles(ADH1B*2 and ADH1C*1) rapidly convert ethanol to acetaldehyde [25]. This leads toacetaldehyde accumulation following alcohol consumption and results in toxic side effects(e.g., flushing syndrome with sweating, accelerated heart rate, nausea, and vomiting) thatdeter the carriers from acute and chronic alcohol consumption (i.e., individuals with thesealleles typically drink little or no alcohol) [26,27] and protect them from alcohol-relatedcancer [28].

The aim of the present study was to investigate the effect of alcohol abuse on TL in PBLs,and to elucidate whether such effect is modified by genetic variants in alcohol metabolicgenes.

MATERIALS AND METHODSStudy participants

The study population (n=457) was composed by Caucasian males living in NortheasternItaly, including 200 alcohol abusers and 257 controls. Study participants were recruited fromNovember 2008 through September 2009. The alcohol abusers were drunk-driving trafficoffenders enrolled in a probation program and referred as outpatients to the forensictoxicology and antidoping ambulatoryof University of Padova. They were defined as alcoholabusers as per the Diagnostic and Statistical Manual of Mental Disorders [DSM-IV-TR] ofthe American Psychiatric Association, i.e., individuals with a maladaptative pattern ofsubstance use as manifested by recurrent alcohol use in situations in which it is physicallyhazardous. All of them were found to be driving a vehicle with blood alcohol concentration[BAC] >1.5gr/L. Except for 11 abstainers, controls were social drinkers with variable

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alcohol use. Controls were members of a group of white-collar workers, recruited duringtheir periodic check-ups at the Unit of Preventive Health Services, University of Padova aspreviously described [29]. Alcohol abusers as per DSM-IV-TR were excluded from thecontrol group. Trained interviewers informed all participants of the study objectives andcollected personal data including drinking habits, job type, possible elevated non-occupational genotoxic-exposures (smoking, diet, environment), and consumption ofvegetables by means of a structured questionnaire, as previously described [29]. The studywas approved by the University of Padova’s Institutional Review Board. All participantsprovided written informed consent. Smokers were defined as individuals who reportedcurrent active smoking. Nonsmokers were defined as never smokers or former-smokers whohad quit smoking at least one year before blood sample collection. Participants donated 5 mlblood sample for TL and genotyping analyses. Blood samples and information from astructured questionnaire were collected from abusers during their follow-up check-up oftheir probation program, i.e., after at least three 3 months from the beginning of theprogram. All data were anonymized after collection of personal data and blood samples.DNA samples, isolated using the Promega Wizard genomic DNA purification kit (Promega,Italy), was available for TL analysis in all the 200 abusers and 257 controls. However, DNAwas available for genotype analysis in only 149 alcohol abusers and 255 controls.

Usual alcohol intake in both abusers (intake since the beginning of the probation program)and controls (average usual intake) was evaluated based on self-reported questionnaire dataand expressed as units of drink/day. Each unit was equivalent to approximately 10–12galcohol intake. High alcohol intake was defined as > 4 drink-units/day (more than 40galcohol/day) [23]. The integrated measure of alcohol drinking [(drink-unit per years ofexposure (drink-years)] and smoking history (pack-years) for abusers was also collected.Individuals with high dietary intake of genotoxins (in particular polycyclic aromatichydrocarbons [PAHs]) were those who reported consumption of charcoaled meat or pizzamore than once a week; individuals with indoor exposure were those who reported at leastone of several exposure sources (i.e., use of fireplace, coal or wood-stove as heating athome; or passive exposure to tobacco smoke) as previously described [29]. We definedparticipants with a job at elevated risk of accident as those with an occupation in a high-riskcategory, as classified by the Italian National regulations in matter of alcohol and correlatedproblems [30].

Serum γ-glutamyltrasferase [GGT] and mean corpuscular volume of erythrocytes [MCV]were measured as conventional biomarkers of alcohol abuse [31]. MCV and GGT, which areincreased in individuals with chronic and heavy alcohol drinking, have been shown to returnwithin normal ranges after complete abstinence for 120 days (MCV) or 15–40 days (GGT)[31]. Abusers were also screened through analysis of serum carbohydrate-deficienttransferrin (CDT; asialo- plus monosialo- plus disialo-Fe2-transferrin), a traditionalbiomarker of alcohol abuse that normalizes after 15–30 days of abstinence [31]. CDTanalysis was performed on P/ACE MDQ capillary electrophoresis systems (BeckmanCoulter) with UV detection at 200 nm (interference filter) and valley to valley integrationperformed with 32 Karat software (Beckman Coulter). Data were evaluated on the basis ofcorrected peak areas (peak area divided by detection time). The amounts of single Tfisoforms and CDT (sum of asialo- and disialo-Tf) were calculated as area % in relation tothe sum of the corrected peak areas of all detected Tf isoforms.

Genotype analysesAfter DNA isolation with a Promega Wizard genomic DNA purification kit (Promega,Italy), was available for genotype analysis for 149 alcohol abuser and 255 controls.Determination of the ADH1C *1/*2 (rs 698) Ile350Val in the exon 8 polymorphism wasperformed following the method based on restriction fragment length polymorphism

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previously described [32]. We used primers, identified as 321 and 351, that allow forexclusive amplification of exon 8 of the ADH1C gene and generate the Ssp I recognitionsequence AATATT as an internal control outside of the tested region. Thus, it is possible todistinguish between the ADH1C*1 allele with fragments (67 and 63 bp) and the ADH1C*2allele with a 130 bp fragment. Alcohol dehydrogenase β subunit (ADH1B) polymorphism*1/*2 (rs1229984 Arg47His) and aldehyde dehydrogenase 2 (ALDH2) (rs 671 Glu487Lys)were genotyped following the method of Tamakoshi et al. [33] by a duplex polymerasechain reaction (PCR). This method confronting two-pair primers (PCR–CTPP) allows forDNA amplification with one-tube PCR including eight primers, and subsequentelectrophoresis in 2% agarose gel. ADH1B His and Arg alleles was determined by presenceof 280 bp and 219 bp fragments. ALDH2 Glu and Lys alleles by presence of 119 bp and 98bp fragments. Quality-control measures were adopted in genotyping, such as validation ofresults by using the TaqMan-based Real-Time PCR method and blind repeat of 10% ofsamples.

TL measurementTL was measured in blood genomic DNA using the multiplex real-time quantitative PCRmethod described by Cawthon [34]. This method is a modification of previous TL analysisreal-time PCR methods that allows for increased reproducibility [34]. This methodmeasuresthe relative telomere length in genomic DNA by determiningthe ratio of telomere repeatcopy number (T) to single copy gene (S)copy number (T/S ratio) in experimental samplesrelative to a reference pool sample [34]. The single copy gene used in this study washumanβ-globin (hbg). The analysis was conducted using a CFX384 real-time PCR detectionsystem (Bio-Rad, Hercules, California, USA). A high-precision MICROLAB STARletRobot (Hamilton Life Science Robotics, Bonaduz AG, Switzerland) was used fortransferring in a 384-well format plate a volume of 5μl reaction mix and 2μl DNA (3 ng/μl).A six points standard curve generated from serially diluted of a pool DNA ranging from 90ng to 0.37 ng, was inserted in every 384-well plate in this study.

For multiplex real-time PCR we used the primer sets previously described by Cawthon [34].A primer pair of beta-globin single copy gene (hbgu and hbgd) were combined with thetelomere primer pair (telg and telc) in the same reaction mix. The multiplex PCR mix was:iQ SYBR Green Supermix (Bio-Rad) 1×, telg 600nM, telc 600nM, hbgu 250nM, hbgd250nM, H2O. The thermal cycling profile started with a 95°C incubation for 3 minutes toactivate the hot-start iTaq DNA polymerase, then 2 cycles of 15 s at 94°C, 15 s at 49°C, and32 cycles of 15 s at 94°C, 10 s at 62°C, 10 s at 74°C with signal acquisition, 10 s at 84°C, 10s at 88°C with signal acquisition At the end of each real-time PCR reaction to verify thespecificity of amplified, a melting curve was added from 72°C to 95°C with an increment0.5°C per step.

All samples were run in triplicate and the average of the three T/S ratio measurements wasused in the statistical analyses. To examine the reproducibility of T/S measurement, werepeated the assay for 20 samples in two different days. The between-day coefficient ofvariation was 3.0%.

Statistical analysisStatistical comparisons were made between the abusers and controls using the non-parametric Mann-Whitney U-test or the Fisher’s exact test. Bivariate linear regression wasused to assess the influence of age, BMI, vegetable intake, smoking, genotoxic exposurefrom diet, and jobs with elevated risk of accident on TL (dependent variable) amongcontrols. The dependent variable was always TL which was log-transformed before theanalysis to approximate normal distribution. We then classified individuals in usual drink

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categories according to frequency of usual alcohol intake (0–1, 2–4, >4 drink-units/day). Weexamined the relation between TL (dependent variable) and usual drink category (0–1, 2–4,>4) by unadjusted and covariate-adjusted multivariable models to test the effects of possibleTL determinants (i.e., age, smoking, BMI, diet, job with elevated risk of accident, genotoxicexposures) on TL and obtained unadjusted and adjusted TL means and 95% ConfidenceIntervals (CIs). TL of all subjects was log-transformed to approximate normal distribution.Consequently, we present TL data as geometric means and 95% Confidence Intervals (CIs).As alcohol abusers and controls differed in their distribution by age (years), BMI (kg/m2)and vegetables (servings/week), current smoking (ever/ex or never) and jobs with elevatedrisk of accident (yes/not), we used multivariable regression models adjusting for all thesevariables. Age, BMI and vegetables, were fitted as a continuous variables. Current smokingand jobs with elevated risk of accident as categorical variables. To determine whether thegenotypes were in Hardy-Weinberg equilibrium, distribution of the observed and expectedgenotype frequencies were compared using a chi-square test. The interaction terms fordrinks x genotype were tested in a multiple linear regression model where the dependentvariable was TL and the independent variables were drink-units and genotype, bothdichotomously coded (<4 and ≥4 drink- units, 0 and 1; ADH1B*1/*2 or *2/*2 andADH1C*1/*1, 0; ADH1B*1/*1 ADH1C*1/*2 or *2/*2, 1), and the interaction term, whichwas the product of the first two variables. Statistical significance for the interaction term wastested using a Wald test. Statistical tests were two-sided, and were performed in Stata 9.0(Stata Corp., College Station, TX).

RESULTSStudy population characteristics and alcohol consumption

The study population included 200 alcohol abusers and 257 controls (Table 1). In average,alcohol abusers were younger than controls (38 vs. 44 years, P<0.001), had moderatelylower BMI (25.5 vs 26.0 Kg/m2, P=0.019), included a higher proportion of current smokers(71% vs. 25%, P<0.001), and were less frequently in jobs at elevated risk of accident (23%vs. 36%, P=0.004). Surprisingly, alcohol abusers reported more frequent vegetableconsumption of vegetables than controls (38% vs. 28% reported eating 7 or more servings ofvegetables/week, P=0.014). Thirteen percent of the alcohol abusers reported a currentconsumption (intake since the beginning of the probation program) of more than 4 drink-units/day of alcohol, compared to 2% of the controls (P<0.001). Conversely, 55% of thealcohol abusers reported a usual consumption of 0–1 drink-units/day, compared to 71% ofthe controls (P<0.001). No correlation between smoking and alcohol was observed inabusers (Spearman’s rank correlation Rho=0.072; p=0.308) while the correlation wassignificant in controls (Spearman’s rank correlation Rho=0.161; p=0.0099). MCV, i.e. theconventional biomarker of alcohol abuse with longer half-life, was more frequently abovethe clinical reference value (≥96 U/L) in abusers (11%) than controls (5%; P=0.021). Theproportion of individuals with levels of GGT, i.e., the biomarker with shorter half-life,above the clinical reference value (≥65 U/L) was not significantly higher in abusers (10%)relative to controls (6%). Abusers were further profiled through serum CDT analysis, ashort-lived biomarker alcohol abuse. Only four (7%) of the abusers were found positive forCDT.

Telomere length in alcohol abusers and controlsPBL-TL was nearly halved in alcohol abusers (Table 2, geometric mean [GM] 0.43 T/S;range: 0.20–1.11) compared to controls (GM 0.87 T/S; range: 0.30–4.84; P<0.0001). The TLdifference was also significant between the 59 nonsmoking alcohol abusers and 192nonsmoking controls (GMs 0.40 T/S vs. 0.79 T/S; z=8.98, p<0.0001), as well as between the141 smoking alcohol abusers and 65smoking controls (GMs 0.44 T/S vs. 1.12 T/S z=8.98,

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p<0.0001). In bivariate linear regression analysis, TL of abusers (n=200) was inverselyassociated with age (p=0.006) and moderately positively associated with smoking (p=0.06),but not with BMI, vegetable intake, genotoxic exposure from diet, and jobs with elevatedrisk. In abusers the correlations between the integrated measure of drinking (drink-years)and smoking (pack-years) on PBL-TL showed that drink-years were moderately associated(p<0.07) with TL shortening, while pack-years were positively associated with TL(p=0.018). TL of controls (n=257) was also positively associated with smoking (cigarettes/day) (p=0.005), but not with age, BMI, vegetable intake, smoking, genotoxic exposure fromdiet, and jobs with elevated risk. GMs adjusted by age, BMI, current smoking, vegetables,and job at elevated risk of accident were 0.42 (0.19–1.10) in alcohol abusers and 0.87 (0.29–4.86) in controls (p<0.001). Fifty-nine (30%) of the alcohol abuser had TL lower than the5th percentile (0.38 T/S) of the controls (unadjusted P<0.0001; adjusted P=0.0005) [Table2]. In the entire study population, PBL-TL decreased in relation with increasing alcoholdrinking [Table 3]. Tests for trend for shortened TL across all drink categories werestatistically significant in both unadjusted (P-trend=0.004) and adjusted (P-trend=0.003)analyses. In particular, subjects drinking >4 drink-units/day had substantially shorter TL(unadjusted GM 0.48 vs. 0.63 T/S, P=0.004; adjusted GM 0.48 vs. 0.61 T/S, P=0.002). PBL-TL did not show significant differences between light (0–1 drink-units/day) and moderate(2–4 drink-units/day) alcohol drinkers (Table 3). However, TL was not associated with usualdrinking categories when abusers and controls were evaluated separately (Table 3).

Telomere length and alcohol metabolic polymorphismsADH1B and ADH1C frequencies were Hardy Weinberg equilibrium (Table 4) and in linewith those found in Caucasians by others authors in a larger Caucasian population [28]. Asexpected, none of the subjects carried the ALDH2 rs671 polymorphism, which is very rareamong Caucasian subjects. Carriers of the ADH1B*2 (rs1229984) [Arg/His or His/His]allele were protected from being abusers [OR=0.28; 95% CI 0.14–0.55; Table 4], drank lessdrink-units/day and showed longer TL (especially among controls and all subjects) (Table5). Conversely carriers of the ADH1B*1 genotypes were more likely to be abusers, drankmore units and showed shorter TL in the entire study population and among controls (Table5). Statistical test for the interaction term drink x genotype ADH1B was significant amongcontrols, as well as in all subjects (P<0.0001), and borderline significant (P=0.054) amongabusers. The rs698 ADH1C were neither associated with TL nor with the number of drink-units, and did not show a statistical interaction with alcohol abuse in determining TL.

DISCUSSIONIn the present work, we found that alcohol abusers had significant shorter TL in PBLscompared to controls, taking into account several other potential determinants of telomereshortening. PBL-TL decreased with the amount of drinking when all study participants wereconsidered together, particularly in subjects drinking >4 drink-units/day.

TL is widely considered a clock of biological age at the cellular level. Epidemiology studieshave shown that among subjects of the same age, those with shorter telomeres in PBLs havehigher risk of age-related diseases, such as cancer [6–14]. Our results show that abuse inalcohol drinking is associated with shortened telomeres suggesting a premature aging at thecellular level as reflected in telomere shortening in PBLs. Drinking more than 4 units ofdrinks/day of wine, has been clearly established as an independent risk factor for cancer [35]while light alcohol drinking was not associated with cancer risk [35]. In our study, we foundshorter telomeres particularly among those individuals who consumed heavy amounts ofalcohol (e.g., more than 4 drinks/day) when data from all participants were evaluatedtogether. To the best of our knowledge this is the first study relating shorter telomeres toabuse in alcohol drinking. Other studies, that examined the relation between alcohol

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drinking and TL in cancer patients [36,37] or in older individuals [38], have found no[36,38] or marginal [37] associations between alcohol intake and TL. However, theinfluence of alcohol drinking on TL in abusers, who are individuals with harmful andunhealthy heavy irregular pattern of drinking, has never been evaluated [39]. Previousstudies have suggested that telomeres are shortened by other risk factors for age-relateddiseases, such as phsychological stress [16], smoking [36,40,41], obesity [40], chronicinflammation [18,19], male gender [6,14], and exposure to particulate air pollution [42] andPAHs [43]. Chances that shorter TL could depend on factors other than the variables ofconcern were minimized in the present study because the study participants were all malesand of similar ethnicity. In addition, we used multivariable models to adjust for potentialconfounding factors, including age, BMI, current smoking, vegetables, job at elevated riskof accident, possible genotoxic exposures from diet, as well as from indoor pollutants. Nosubjects were affected by chronic diseases, including liver cirrhosis [44]. In alcohol abusersor controls separately, however no associations between units of drink and TL were found.Taken together, these results suggest that the condition of being an alcohol abuser, ratherthan the amount of drinking, is associated with shorter TL. Irregular patterns of alcoholdrinking, such as those found among alcohol abusers, have been linked with increasedcancer risk [35] as opposed to the favorable effects of moderate and regular alcoholconsumption. It is possible, however, that psychological stress [16], as well as otherconditions or lifestyle factors associated to being an alcohol abuser or driving underinfluence might have contributed in the reduction of PBL-TL that we observed in alcoholabusers.

In our study we found a statistically significant association between age and reduction inPBL-TL of alcohol abusers but not in controls. Loss in telomere length is most pronouncedin childhood and old age, with a more gradual attrition in mid-life [5]. We have analyzed TLin controls that presented a limited age range (25–62 years) compared to that of abusers (35–75 years). This could limit our capacity to identify an association between age and TL incontrols. Study subjects of similar age generally display a large variation in telomere length.Thus, a wider age range, as well as a larger sample size, might be necessary to detect asignificant correlation between telomere length and age in healthy subjects such as those inour control group.

Moreover, in our study we found that smoking significantly increases PBL-TL both inabusers and controls. Several studies [8,17,42,45,46], including large investigations such asthat conducted by Bischoff et al. [45] and Cassidy et al., 2010 [46], were unable to confirmthe negative correlation between PBL-TL and smoking found by others [36,40,41]. Theinconsistent results likely reflect a moderate effect of smoking, if any, on PBL-TL thatmight not be easily detectable. Experimental in-vitro models have shown that duringinflammation, which is a central process in mediating health effects from smoking exposure[47], telomere length increase in younger inflammatory T cells [48]. Increased TL we foundin current smokers could be attributed to the recruitment of younger inflammatory cells, whohave longer TL, from the bone marrow in the bloodstream in response to inflammatory cues[48] such as those associated with smoking [47]. However, the association of alcohol abusewith attrition in PBL-TL in our study was independent of smoking, as demonstrated byanalyses stratified by smoking status as well as by multivariable models.

Carriers of the ADH1B*2 (rs1229984) allele were protected from being abusers, dranksignificantly less and showed longer TL than those with common wild-type homozygousgenotype ADH1B*1. Conversely those with the ADH1B*1 were more likely to be abusers,drank more and showed shorter TL. Alcohol is primarily metabolized by the ADH andALDH2 enzymes. Among Caucasians, variants in ADH genes are common, whereas those inALDH2 are very rare. In particular, ADH1B*2 codes for an enzyme 40-fold more active than

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that encoded by the ADH1B*1 allele, whereas ADH1C*1 is only 2 or 3 times moreactive[25], and is associated with large production of acetaldehyde and a correspondingflush syndrome which deters carriers from drinking alcohol [26,27]. Our results are in linewith previous studies that found an association between the ADH1B*2 allele and protectionagainst alcoholism. A large study that analyzed six ADH polymorphisms showed thatADH1B*2 was the genotype that conferred the strongest protection against alcohol-relatedcancers [28]. Alcohol, if not quickly metabolized to acetaldehyde, may be alternativelymetabolized by CYP2E1, the main hepatic alcohol-inducible cytochrome, which is capableof producing large amounts of radical oxygen species (ROS) [49]. The induction of CYP2E1by methanol increases ROS formation (large amounts of O2− and H2O2· reduced) throughthe activity of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity [23].Moreover nitric oxide synthase is also induced by alcohol leading to the formation of nitricoxide and highly-reactive peroxynitrite (ONOO−) [50]. Lastly, heavy drinking episodeswere associated with an unfavourable lipid profile (decrease in high density lipoproteins,increased in low density lipoproteins) that contributes to generate ROS [39]. Through allthese pathways, alcohol may create an oxidative microenvironment, suitable for generatingspecific telomeric damaging agents and, in general, conditions suitable for the developmentof related pathologies with such as cancer [23]. Telomeres, as triple-guanine-containingsequences, are highly sensitive targets for damage by oxidative stress [15]. It could behypothesized that high levels of 8-hydroxy-2′-deoxyguanosine (8-OH-dG) the moreabundant species of the altered DNA bases when DNA is oxidatively modified by ROS)may be formed from metabolism of abuse in alcohol drinking. Damaging 8-OH-dGformation can induce a reduction in TL by double-strand breaks and/or interference withreplication fork [15].

In conclusion, our results show that abuse in alcohol drinking is associated with shortenedtelomeres which suggests a premature aging at the cellular level as reflected in telomereshortening in PBLs. ADH1B*1 carriers exhibited higher frequency of alcohol abuse, drunkmore and present shorter telomeres. Thus, individuals with shorter telomere could be athigher risk of an earlier onset of cancer and this risk may be modulated by the alcoholmetabolic ADH1B*1/*2 genotype. Our findings provide additional biological knowledgethat could be used by clinicians to deter subjects from a maladaptative pattern of alcoholuse. Future studies are warranted to further determine the mechanisms linking telomereshortening to the risk of alcohol-related cancers.

AcknowledgmentsFunding/Support: The research reported in this article was supported by Universita di Padova, Ricerca di Ateneo,Anno: 2007 - prot. CPDA072111; Italian Association for Research against Cancer (AIRC IG-6016), CARIPLOFoundation (2007-5469); New Investigator funding from the HSPH-NIEHS Center for Environmental Health(ES000002). Dr. Baccarelli’salary is supported in part by New Investigator funding from the HSPH-NIEHS Centerfor Environmental Health (ES000002).

Role of the Sponsors: None of the funding agencies had any role in the design and conduct of the study, in thecollection, management, analysis, and interpretation of the data, or in the preparation, review, or approval of themanuscript.

Abbreviations

ALT alanine aminotrasferase

AST aspartate aminotrasferase

CDT carbohydrate-deficient transferring

GGT serum γ-glutamyltrasferase

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MCV mean corpuscular volume of erythrocytes

PBLs peripheral blood leucocytes

PAHs polycyclic aromatic hydrocarbons

TL telomere length

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Table 1

Characteristics of the study population a.

Alcohol abusers (Nb=200) Controls (N=257) P valuee

Age (years)

Mean (Range) 38(35–75) 44(25–62) <0.0001

Body mass index (kg/m2)

Mean (Range) 25.5(18.9–45.0) 26.0(19.8–38.0) 0.019

Current smoking habits

Smokers N (%) 141(71%) 65(25%) <0.0001

c Cigs/day Mean (Range) 17.5(0.14–60) 14.6(0.28–40) 0.097

Genotoxic exposure through diet (times/week)

N(%) of subjects with intake of genotoxin-rich food > once a week 111(56) 168(65) 0.986

Vegetables (servings/week)

Low (0–3) N(%) 69(35) 112(44)

Medium (4–6) N(%) 54(27) 73(28) 0.014

High (7 and more) N(%) 77(38) 72(28)

Exposure indoor pollutant

Not exposed N(%) 135(68) 198(77)

Low N(%) 58(29) 51(20) 0.490

High N(%) 7(4) 8(3)

Subjects with jobs at elevated risk of accident

N(%) 46(23) 92(36) 0.004

Indicators of alcohol intake and effects

Usual drinks (drink-units/day)mean (range) 1.93(0.04–10) 1.31(0–5.4) 0.049

Usual drinking category (drink-units/day)

N(%)0–1 110(55) 182(71)

N(%)2–4 65(32) 70(27) <0.0001

N(%)>4 25(13) 5(2)

MCV (fL) (normal value: 80,0–96,0)

Mean (Range) 89 (60–102) 89(62–106) 0.187

N(%)d≥96.0 22/181(11) 14/255(5) 0.021

GGT(U/L) (normal value: 3–65)

Mean (Range) 34(5–320) 32(6–172) 0.364

N(%)d65 18/182(10) 19(6) 0.356

aAll the study participants were Caucasian males.

bN, number.

cCigs, cigarettes.

dNumber after/indicate the numbers of subjects we have obtained the informations.

eMann-Whitney U test and Fisher’s exact test.

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Tabl

e 2

Telo

mer

e le

ngth

in a

lcoh

ol a

buse

rs a

nd c

ontro

ls.

Alc

ohol

abu

sers

Con

trol

sSt

atis

tics

n=20

0n=

257

P va

lue

Una

djus

ted

P-va

lue

Adj

uste

d†

Telo

mer

e le

ngth

(T/S

)

Geo

met

ric m

ean

(ran

ge)*

0.43

(0.2

0–1.

11)

0.87

(0.3

0–4.

84)

<0.0

001

<0.0

001

N (%

) ≤ 5

° per

cent

ile T

L va

lue

of c

ontro

ls (0

.38

T/S)

59 (3

0)14

( 5)

<0.0

001

0.00

05

* Una

djus

ted

geom

etric

mea

ns. G

eom

etric

mea

ns a

djus

ted

by a

ge, B

MI,

curr

ent s

mok

ing,

veg

etab

les,

and

job

at e

leva

ted

risk

of a

ccid

ent w

ere

0.42

in a

lcoh

ol a

buse

rs a

nd 0

.87

in c

ontro

ls.

† Adj

uste

d by

age

, BM

I, cu

rren

t sm

okin

g, v

eget

able

s, jo

b at

ele

vate

d ris

k of

acc

iden

t

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Tabl

e 3

Telo

mer

e le

ngth

acc

ordi

ng to

usu

al d

rinki

ng c

ateg

orie

s

Usu

al d

rink

ing

cate

gory

Tel

omer

e le

ngth

, T/S

Geo

met

ric

mea

n(9

5% C

I)p-

valu

ep-

tren

d

All

Subj

ects

(n=4

57)

Una

djus

ted

0–1

drin

k-un

its/d

ay0.

67(0

.62–

0.71

)R

ef.

2–4

drin

k-un

its/d

ay0.

63(0

.57–

0.69

)0.

33

>4 d

rink-

units

/day

0.48

(0.3

9–0.

59)

0.00

40.

004

Adj

uste

d*

0–1

drin

k-un

its/d

ay0.

67(0

.63–

0.72

)R

ef.

2–4

drin

k-un

its/d

ay0.

61(0

.56–

0.68

)0.

14

>4 d

rink-

units

/day

0.48

(0.3

9–0.

59)

0.00

20.

003

Abu

sers

(n=2

00)

Una

djus

ted

0–1

drin

k-un

its/d

ay0.

44(0

.41–

0.46

)R

ef.

2–4

drin

k-un

its/d

ay0.

43(0

.40–

0.46

)0.

67

>4 d

rink-

units

/day

0.42

(0.3

8–0.

46)

0.41

0.48

Adj

uste

d*

0–1

drin

k-un

its/d

ay0.

43(0

.41–

0.45

)R

ef.

2–4

drin

k-un

its/d

ay0.

43(0

.41–

0.46

)0.

91

>4 d

rink-

units

/day

0.43

(0.3

9–0.

48)

0.95

0.92

Con

trols

(n=2

57)

Una

djus

ted

0–1

drin

k-un

its/d

ay0.

86(0

.79–

0.94

)R

ef.

2–4

drin

k-un

its/d

ay0.

89(0

.77–

1.02

)0.

68

>4 d

rink-

units

/day

0.97

(0.5

8–1.

62)

0.66

0.58

Adj

uste

d*

0–1

drin

k-un

its/d

ay0.

88(0

.81–

0.96

)R

ef.

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Usu

al d

rink

ing

cate

gory

Tel

omer

e le

ngth

, T/S

Geo

met

ric

mea

n(9

5% C

I)p-

valu

ep-

tren

d

2–4

drin

k-un

its/d

ay0.

83(0

.73–

0.96

)0.

45

>4 d

rink-

units

/day

0.88

(0.5

3–1.

45)

0.99

0.55

* Adj

uste

d by

age

, BM

I, cu

rren

t sm

okin

g, v

eget

able

s, el

evat

ed ri

sk o

f acc

iden

t

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Tabl

e 4

ADH

1B, A

DH

1C a

nd A

LDH

2 fr

eque

ncie

s and

OR

in a

buse

drin

kers

and

con

trols

Abu

se d

rink

ers

Con

trol

s

Gen

otyp

esO

bser

ved

num

ber(

%)

Exp

ecte

d nu

mbe

r(%

)aO

bser

ved

num

ber(

%)

Exp

ecte

d nu

mbe

r(%

)ab

OR

(95%

CIs

)

ADH

1B (r

s122

9984

)

*1/*

113

6(91

)13

6(92

)19

1(75

)19

2(75

)R

ef.

*1/*

213

(9)

12(8

)61

(24)

58(2

3)0.

28(0

.14–

0.55

)**

*2/*

23(

1)4(

2)

ADH

1C (r

s698

)

*1/*

160

(40)

61(4

1)10

6(42

)10

3(41

)R

ef.

*1/*

270

(47)

69(4

6)11

0(44

)11

6(46

)0.

92(0

.60–

1.42

)

*2/*

219

(13)

20(1

3)35

(14)

32(1

3)

ALD

H2

(rs6

71)

*1/*

114

9(10

0)-

257(

100)

-N

D

*1/*

2

*2/*

2

a Acc

ordi

ng to

Har

dy –

Wei

nber

g

b Odd

s Rat

ios (

OR

s) a

nd 9

5% C

onfid

ence

Inte

rval

s (95

% C

Is) w

ere

calc

ulat

ed u

sing

the

Fish

er’s

Exa

ct m

etho

d

**P=

0.00

8.

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Tabl

e 5

Usu

al d

rinks

and

telo

mer

e le

ngth

in a

lcoh

ol a

buse

rs, c

ontro

ls a

nd a

ll su

bjec

ts a

ccor

ding

to A

DH

1B (r

s122

9984

) and

AD

H1C

(rs6

98) g

enot

ypes

.

Alc

ohol

abu

sers

Con

trol

sA

ll Su

bjec

ts

Gen

otyp

es

Dri

nk- u

nits

/day

≥4

drin

k- u

nits

/day

Tel

omer

e le

ngth

Dri

nk- g

enot

ype

cD

rink

- uni

ts/d

ay≥

4 dr

ink-

uni

ts/d

ayT

elom

ere

leng

thD

rink

- gen

otyp

ecD

rink

- uni

ts/d

ay≥

4 dr

ink-

uni

ts/d

ayT

elom

ere

leng

thD

rink

- gen

otyp

e c

N a

Med

ian

(ran

ge)

N (%

)G

M b

(ran

ge)

r P

NM

edia

n (r

ange

)N

(%)

GM

(ran

ge)

r P

NM

edia

n (r

ange

)N

(%)

GM

(ran

ge)

r P

ADH

1B *

1/*2

(Arg

47H

is)

*1/*

113

61.

14 (0

.04–

10.0

)24

(18)

0.41

(0.2

3–0.

86)

0.15

90.

054

191

1.00

(0–5

.40)

15 (8

)0.

83 (0

.32–

4.85

)0.

34<0

.001

327

1 (0

–10.

0)39

(12)

0.62

(0.2

3–4.

85)

0.35

<0.0

001

*1/*

2 or

*2/

*213

0.57

# (0

.14–

3.00

)0

(0)

0.44

(0.3

4–0.

53)

640.

90° (

0–4.

00)

2 (3

)1.

01§

(0.3

0–4.

29)

770.

86##

(0–4

.00)

2°° (

3)0.

88§§

(0.3

0–4.

29)

ADH

1C *

1/*2

(Ile

350V

al)

*1/*

2 or

*2/

*289

1.00

(0.0

4–10

.0)

15 (1

7)0.

42 (0

.23–

0.86

)0.

040.

649

145

1.00

(0–5

.40)

10 (7

)0.

90 (0

.33–

4.85

)0.

020.

729

234

1 (0

–10.

0)25

(11)

0.67

(0.2

3–4.

85)

0.01

0.79

3*1

/*1

601.

07 (0

.14–

9.00

)9

(15)

0.42

(0.2

4–0.

78)

106

1.00

(0–5

.29)

7 (7

)0.

82 (0

.30–

4.06

)16

61

(0–9

.00)

16 (1

0)0.

65 (0

.24–

4.0

6)

a N, n

umbe

rs o

f sub

ject

s.

b GM

, geo

met

ric m

ean.

c The

inte

ract

ion

term

s for

drin

ks x

gen

otyp

e w

ere

test

ed in

a m

ultip

le li

near

regr

essi

on m

odel

whe

re th

e de

pend

ent v

aria

ble

was

TL

and

the

inde

pend

ent v

aria

bles

wer

e dr

ink-

units

and

gen

otyp

e, b

oth

dich

otom

ousl

y co

ded

(<4

and ≥

4 dr

ink-

uni

ts, 0

and

1; A

DH

1B*1

/*2

or *

2/*2

and

AD

H1C

*1/*

1, 0

; AD

H1B

*1/*

1 A

DH

1C*1

/*2

or *

2/*2

, 1),

and

the

inte

ract

ion

term

, whi

ch w

as th

e pr

oduc

t of t

he fi

rst t

wo

varia

bles

. ( r)

cor

rela

tion

coef

ficie

nt a

nd (P

) cal

cula

ted

prob

abili

ty b

y W

ald

test

.

Man

n-W

hitn

ey U

test

° Z= 2

.49

P=0.

0128

;

# Z= 2

.49

P=0.

0126

,

##Z=

3.6

25P=

0.00

03;

§ Z=2.

05 P

=0.0

40;

§§Z=

3.95

P<0

.000

1; F

ishe

r’s E

xact

met

hod

°°P=

0.00

8.

Int J Cancer. Author manuscript; available in PMC 2012 August 15.